Creation and arrangement of items in an online concierge system-specific portion of a warehouse for order fulfillment

ABSTRACT

A warehouse from which shoppers fulfill orders for an online concierge system maintains an online concierge system-specific portion for which the online concierge system specifies placement of items in regions. To place items in the online concierge system-specific portion, the online concierge system accounts for co-occurrences of different items in orders and measures of similarity between different items. From the co-occurrences of items, the online concierge system generates an affinity graph. The online concierge system also generates a colocation graph based on distances between different regions in the online concierge system-specific portion. Using an optimization function with the affinity graph and the colocation graph, the online concierge system selects regions within the online concierge system-specific portion for different items to minimize an amount of time for shoppers to obtain items in the online concierge-system specific portion.

BACKGROUND

This disclosure relates generally to order fulfillment by an onlineconcierge system and more specifically to creating and stocking items inan online concierge system-specific portion of a warehouse for orderfulfillment by shoppers.

Conventional warehouses, such as retail stores, are configured toincrease opportunities for a warehouse to obtain revenue from customers.For example, conventional warehouses arrange items to increaseopportunities to browse through items offered by a warehouse whenshopping, increasing likelihoods of customers purchasing more productswhen navigating through the warehouse. While such arrangement ofphysical items allows greater revenue opportunities for a warehouse, itoften increases an amount of time for customers to find specific itemsin the warehouse. For a shopper fulfilling an order for an onlineconcierge system, such an increased amount of time to find an item in awarehouse increases an overall amount of time for the shopper to obtainitems for an order, which increases a likelihood of the shopperfulfilling the order later than a time specified by a user.

SUMMARY

To simplify retrieval of items for orders, the online concierge systemand a warehouse establish an online concierge system-specific portion ofthe warehouse. The online concierge system provides the warehouse withinstructions for arranging items within the online conciergesystem-specific portion of the warehouse. This allows the onlineconcierge system to arrange items in the online conciergesystem-specific portion of the warehouse to reduce amounts of time forshoppers to obtain orders from the online concierge system-specificportion of the warehouse, decreasing fulfillment time for ordersreceived by the online concierge system that identify the warehouse.This also helps shoppers find items in an order more easily, therebyincreasing the findability or find rate of items that customers haveordered. In various embodiments, the online concierge system-specificportion of the warehouse comprises an aisle, or multiple aisles, withinthe warehouse.

In various embodiments, the online concierge system-specific portion ofthe warehouse is segmented into multiple regions by the online conciergesystem, with each region uniquely identified by a region identifier. Forexample, a region identifier includes an aisle identifier of an aislewithin the online concierge system-specific portion of the warehouse, arow identifier specifying a row on the aisle corresponding to the aisleidentifier, and a column identifier specifying a column of the aislecorresponding to the aisle identifier. However, in other embodiments,the online concierge system uses other information to identify differentregions within the online concierge system-specific portion of thewarehouse. The online concierge system identifies items to be placed indifferent regions and identifies one or more regions to a shopperfulfilling an order to simplify retrieval of items by the shopper.

To arrange items in regions within the online concierge system-specificportion of the warehouse, the online concierge system retrieves orderspreviously fulfilled by the online concierge system via the warehouse.From the previously fulfilled orders, the online concierge systemgenerates an affinity graph comprising nodes that represent itemsoffered by the warehouse and connections between the items representedby the nodes. In various embodiments, the online concierge systemselects a set of candidate items and generates the affinity graphincluding the candidate items and connections between the candidateitems. In some embodiments, the online concierge system selects the setof candidate items based on the top ordered items from historicalinformation. In other embodiments the online concierge system selectsthe set of candidate items based on a prediction of the number of ordersthat will including the items. For example, the online concierge systemapplies a trained prediction model to items offered by a warehouse todetermine a predicted number of orders that are likely to include eachitem.

The online concierge system determines an affinity score between a firstitem and a second item based on characteristics of the first and seconditems. For example, an affinity score between the first item and thesecond item is based on a co-occurrence score for the first item and thesecond item. The co-occurrence score between a first item and a seconditem is based on a number of previously fulfilled orders including thefirst item and the second item. In an example, the online conciergesystem determines the co-occurrence score between the first item and thesecond item by determining a number of previously fulfilled ordersincluding both the first item and the second item and determining a sumof a number of items including the first item and a number of itemsincluding the second item. The co-occurrence score between the item andthe additional item is determined by dividing the number of ordersincluding both the first item and the second item by the sum. In someembodiments, the online concierge system determines the co-occurrencescore between the first item and the second item by multiplying thenumber of previously fulfilled orders including both the first item andthe second item by a constant (e.g., 2) and dividing the resultingproduct by a sum of a number of items including the first item and anumber of items including the second item. In some embodiments, theaffinity score between the first item and the second item is theco-occurrence score of the first item and the second item.

The affinity score between the first item and the second item mayaccount for other characteristics between the first item and the seconditem in other embodiments. For example, the affinity score between twoitems may also be computed based on a measure of how dissimilar the twoitems are from each other. The measure of how dissimilar two items aremay be inversely related to a similarity score for the two items, suchthat the affinity score between two items is higher when the similarityscore between the two items is lower. In various embodiments, the onlineconcierge system determines item embeddings for each item offered by awarehouse, and the measure of similarity between a first item and asecond item is a measure of similarity between a first item embeddingfor the first item and a second item embedding for the second item.Example measures of similarity between item embeddings include a cosinesimilarity or a dot product between the item embeddings. In otherembodiments, the online concierge system determines the measure ofsimilarity between the first item and the second item based on a numberof common attributes of the item and the additional item. The onlineconcierge system may determine the affinity score between the first itemand the second item by combining the co-occurrence score of the firstitem and the second item and the measure of similarity between the firstitem and the second item. The affinity score between the first item andthe second item is stored as a weight of a connection between the firstitem and the second item when generating the affinity graph. In otherembodiments, the online concierge system stores the co-occurrence scoreand the measure of similarity as weights of a connection between thefirst item and the second item when generating the affinity graph.

In some embodiments, the online concierge system generates clusters ofitems. In various embodiments, the online concierge system useshierarchical clustering to generate the clusters of items. The onlineconcierge system generates clusters so items included in a cluster havemaximum affinity scores with each other. The online concierge systemgenerates clusters of items based on distances between items, withclusters including items having less than a threshold distance betweeneach other in the affinity graph or including items so distances betweenitems in the cluster is minimized. To generate clusters, the onlineconcierge system determines distances between pairs of items using ameasure of similarity between the items of the pair and theco-occurrence score between the items of the pair. For example, theonline concierge system determines a distance between a first item and asecond item by dividing the measure of similarity between the first itemand the second item by the affinity score of the first and second items.Using the determined distances between items, the online conciergesystem applies one or more clustering models to generate clusters. Invarious embodiments, using hierarchical clustering identifies a seriesof hierarchical groups for each item included in the affinity graph;hence, an item is associated with different clusters that correspond todifferent levels in a taxonomy. In various embodiments, different groupsof the series correspond to different levels in a taxonomy maintained bythe online concierge system.

While the affinity graph and the clusters of items allows the onlineconcierge system to identify relationships between items and generateclusters of items with high co-occurrence scores and low measures ofsimilarities, the online concierge system also generates a colocationgraph describing a physical layout of the online conciergesystem-specific portion of the warehouse. In various embodiments, thecolocation graph allows the online concierge system to account fordistances between different regions of the online conciergesystem-specific portion of the warehouse, which allows the onlineconcierge system to account for travel time between regions of theonline concierge system-specific portion of the warehouse for shoppers.The colocation graph identifies each region within the online conciergesystem-specific portion of the warehouse and maintains connectionsbetween different pairs of regions within the online conciergesystem-specific portion of the warehouse. A weight of a connectionbetween a region within the online concierge system-specific portion ofthe warehouse and an additional region within the online conciergesystem-specific portion of the warehouse is a distance between theregion and the additional region. In various embodiments, the onlineconcierge system generates and stores a colocation graph correspondingto different warehouses, allowing the online concierge system to accountfor different configurations of different warehouses.

The online concierge system leverages the clustering and the colocationgraph to determine placement of items in regions of the online conciergesystem-specific portion of the warehouse. Using the clustering and thecolocation graph allows the online concierge system to generateinstructions for placing items in different regions of the onlineconcierge system-specific portion of the warehouse that reduces amountsof time for shoppers to retrieve items from the warehouse whenfulfilling orders. In various embodiments, the online concierge systemapplies one or more greedy optimization methods to combinations of pairsof items and regions within the online concierge system-specific portionof the warehouse subject to an optimization function.

In various embodiments, the optimization function accounts for distancesbetween regions within the online concierge system-specific portion ofthe warehouse, co-occurrences of items, measures of similarity betweenitems, predicted numbers of orders including items, and rates at whichitems have been found by shoppers. In various embodiments, the onlineconcierge system applies a trained prediction model to numbers of ordersincluding an item at different times to predict a number of ordersincluding the item. The prediction model may be trained from examplescomprising different time intervals labeled with numbers of ordersreceived during a time interval including the item. In variousembodiments, the prediction model is a classical time series model,while in other embodiments the prediction model is a neural networktrained by backpropagation of an error term based on a loss functionbetween a label applied to an example and a predicted number of ordersthrough layers of the neural network until one or more conditions aresatisfied. Further, a rate at which an item has been found by shoppersis based on a previously received number of orders including the itemand a number of previously fulfilled orders including the item in whicha shopper obtained the item. For example, the rate at which the item hasbeen found is determined by dividing the number of previously fulfilledorders including the item in which a shopper obtained the item by thenumber of previously received orders including the item. In variousembodiments, the rate at which an item has been found is determined fora particular time interval or determined from previously received ordersincluding the item obtained (or fulfilled) that satisfy one or morecriteria.

For a combination of an item at a region within the online conciergesystem-specific portion of the warehouse and an additional item at anadditional region within the online concierge system-specific portion ofthe warehouse, the online concierge system determines a co-occurrencevalue as a product of a distance between the region and the additionalregion, the co-occurrence score of the item and the additional item, apredicted number of orders including the item, and a predicted number oforders including the additional item. The online concierge system alsodetermines a similarity value by multiplying the measure of similaritybetween the item and the additional item, a predicted number of ordersin which the item was found, and a predicted number of orders in whichthe additional item was found. In the preceding example, the onlineconcierge system determines the optimization function for thecombination by dividing the co-occurrence value by the similarity value.In various embodiments, the online concierge system applies theoptimization function to each combination of pairs of items and regionswithin the online concierge system-specific portion of the warehouse.For example, the online concierge system selects a cluster of items,generates each pair of items of the cluster and region of the onlineconcierge system-specific portion of the warehouse, and applies theoptimization function to each combination of two pairs of items of thecluster and region of the online concierge system-specific portion ofthe warehouse. In embodiments where the items are hierarchicallyclustered, the online concierge system selects a cluster at a lowestlevel in the hierarchy and applies the optimization function to eachcombination of two pairs of regions within the online conciergesystem-specific region of the warehouse and an item of the cluster.

In various embodiments, the online concierge system selects combinationsof pairs of items and regions within the online conciergesystem-specific portion of the warehouse based on the values for thecombinations from the optimization function. A selected combination ofpairs includes a first pair of an item and a region within the onlineconcierge system-specific portion of the warehouse and a second pair ofan additional item and an additional region within the online conciergesystem-specific portion of the warehouse. Hence, a selected combinationcorresponds to placement of an item at a region within the onlineconcierge system-specific portion of the warehouse and of an additionalitem at an additional region within the online concierge system-specificportion of the warehouse. The online concierge system generates theinstructions for placing items within the online conciergesystem-specific portion of the warehouse from the selected combinations.For example, the instructions include an item identifier and acorresponding identifier of a region within the online conciergesystem-specific portion of the warehouse to specify placement of an itemcorresponding to the item identifier at the region corresponding to theidentifier of the region within the online concierge system-specificportion of the warehouse. In other embodiments, multiple items may beplaced in the same system-specific portion of the warehouse.

The online concierge system transmits the instructions for placing itemswithin regions of the online concierge system-specific portion of thewarehouse to a client device or to another computing device associatedwith the warehouse. Based on the instructions, the warehouse placesitems in regions of the online concierge system-specific portion of thewarehouse. As the instructions for placing items in regions of theonline concierge system-specific portion of the warehouse specifyphysical locations of items in the online concierge system-specificportion of the warehouse, generating the instructions from theoptimization function accounting for characteristics of items and ofregions within the online concierge system-specific portion of thewarehouse allows items to be placed in regions that limits an amount oftime (or an amount of distance) for a shopper to retrieve items from theonline concierge system-specific portion of the warehouse.

After transmitting instructions for placing items within regions of theonline concierge system-specific portion of the warehouse, when theonline concierge system receives a selection of an order by a shopper tofulfill an order at the warehouse, the online concierge system transmitsinstructions to a client device of the shopper for obtaining itemsincluded in the order from within the warehouse. The transmittedinstructions identify an item included in the online conciergesystem-specific portion of the warehouse and a corresponding region ofthe online concierge system-specific portion of the warehouse from whichthe item is obtained. As the items in the online conciergesystem-specific portion of the warehouse are placed by the onlineconcierge system to minimize an amount of time or a distance traveled bya shopper to retrieve the items, the shopper is able to more rapidlyacquire items from the online concierge system-specific portion of thewarehouse. Moreover, the shopper may be able to locate the item moreeasily as an item will tend to be easily distinguishable from otheritems next to it.

Additionally, when a shopper obtains an item from the online conciergesystem-specific portion of the warehouse, the shopper captures an imageof the region of the online concierge system-specific portion of thewarehouse from which the item was obtained via a client device. Theonline concierge system receives the image from the client device. Theonline concierge system applies one or more image processing methods todetermine one or more regions of the online concierge system-specificportion of the warehouse in the image and identifies items included inthe one or more regions of the online concierge system-specific portionof the warehouse in the image. As the online concierge system determinedwhich items were placed in various regions within the online conciergesystem-specific portion of the warehouse, analyzing the regions of theonline concierge system-specific portion of the warehouse allows theonline concierge system to determine inventory levels of items placed indifferent regions of the online concierge system-specific portion of thewarehouse. In various embodiments, the online concierge system updates amachine-learned item availability model based on inventory of itemsdetermined from the received information, allowing the online conciergesystem to improve accuracy of the machine-learned item availabilitymodel by providing more frequent information to the online conciergesystem about inventory of items within the online conciergesystem-specific portion of the warehouse.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system environment in which an onlinesystem, such an online concierge system, operates, according to one ormore embodiments.

FIG. 2 illustrates an environment of an online shopping conciergeservice, according to one or more embodiments.

FIG. 3 is a diagram of an online shopping concierge system, according toone or more embodiments.

FIG. 4A is a diagram of a customer mobile application (CMA), accordingto one or more embodiments.

FIG. 4B is a diagram of a shopper mobile application (SMA), according toone or more embodiments.

FIG. 5 is a flowchart of a method for an online concierge systemarranging items in an online concierge system-specific portion of awarehouse, according to one or more embodiments.

FIG. 6 shows an example affinity graph between items generated by anonline concierge system, according to one or more embodiments.

FIG. 7 is an example colocation graph identifying distances betweenregions of an online concierge system-specific portion of a warehouse,according to one or more embodiments.

FIG. 8 is a process flow diagram of placement of items within regions ofan online concierge system-specific portion of a warehouse using anoptimization function, according to one or more embodiments.

The figures depict embodiments of the present disclosure for purposes ofillustration only. Alternative embodiments of the structures and methodsillustrated herein may be employed without departing from theprinciples, or benefits touted, of the disclosure described herein.

DETAILED DESCRIPTION System Architecture

FIG. 1 is a block diagram of a system environment 100 in which an onlinesystem, such as an online concierge system 102 as further describedbelow in conjunction with FIGS. 2 and 3 , operates. The systemenvironment 100 shown by FIG. 1 comprises one or more client devices110, a network 120, one or more third-party systems 130, and the onlineconcierge system 102. In alternative configurations, different and/oradditional components may be included in the system environment 100.Additionally, in other embodiments, the online concierge system 102 maybe replaced by an online system configured to retrieve content fordisplay to users and to transmit the content to one or more clientdevices 110 for display.

The client devices 110 are one or more computing devices capable ofreceiving user input as well as transmitting and/or receiving data viathe network 120. In one embodiment, a client device 110 is a computersystem, such as a desktop or a laptop computer. Alternatively, a clientdevice 110 may be a device having computer functionality, such as apersonal digital assistant (PDA), a mobile telephone, a smartphone, oranother suitable device. A client device 110 is configured tocommunicate via the network 120. In one embodiment, a client device 110executes an application allowing a user of the client device 110 tointeract with the online concierge system 102. For example, the clientdevice 110 executes a customer mobile application 206 or a shoppermobile application 212, as further described below in conjunction withFIGS. 4A and 4B, respectively, to enable interaction between the clientdevice 110 and the online concierge system 102. As another example, aclient device 110 executes a browser application to enable interactionbetween the client device 110 and the online concierge system 102 viathe network 120. In another embodiment, a client device 110 interactswith the online concierge system 102 through an application programminginterface (API) running on a native operating system of the clientdevice 110, such as IOS® or ANDROID™.

A client device 110 includes one or more processors 112 configured tocontrol operation of the client device 110 by performing functions. Invarious embodiments, a client device 110 includes a memory 114comprising a non-transitory storage medium on which instructions areencoded. The memory 114 may have instructions encoded thereon that, whenexecuted by the processor 112, cause the processor to perform functionsto execute the customer mobile application 206 or the shopper mobileapplication 212 to provide the functions further described above inconjunction with FIGS. 4A and 4B, respectively.

The client devices 110 are configured to communicate via the network120, which may comprise any combination of local area and/or wide areanetworks, using both wired and/or wireless communication systems. In oneembodiment, the network 120 uses standard communications technologiesand/or protocols. For example, the network 120 includes communicationlinks using technologies such as Ethernet, 802.11, worldwideinteroperability for microwave access (WiMAX), 3G, 4G, 5G, code divisionmultiple access (CDMA), digital subscriber line (DSL), etc. Examples ofnetworking protocols used for communicating via the network 120 includemultiprotocol label switching (MPLS), transmission controlprotocol/Internet protocol (TCP/IP), hypertext transport protocol(HTTP), simple mail transfer protocol (SMTP), and file transfer protocol(FTP). Data exchanged over the network 120 may be represented using anysuitable format, such as hypertext markup language (HTML) or extensiblemarkup language (XML). In some embodiments, all or some of thecommunication links of the network 120 may be encrypted using anysuitable technique or techniques.

One or more third party systems 130 may be coupled to the network 120for communicating with the online concierge system 102 or with the oneor more client devices 110. In one embodiment, a third party system 130is an application provider communicating information describingapplications for execution by a client device 110 or communicating datato client devices 110 for use by an application executing on the clientdevice. In other embodiments, a third party system 130 provides contentor other information for presentation via a client device 110. Forexample, the third party system 130 stores one or more web pages andtransmits the web pages to a client device 110 or to the onlineconcierge system 102. The third party system 130 may also communicateinformation to the online concierge system 102, such as advertisements,content, or information about an application provided by the third partysystem 130.

The online concierge system 102 includes one or more processors 142configured to control operation of the online concierge system 102 byperforming functions. In various embodiments, the online conciergesystem 102 includes a memory 144 comprising a non-transitory storagemedium on which instructions are encoded. The memory 144 may haveinstructions encoded thereon corresponding to the modules further belowin conjunction with FIG. 3 that, when executed by the processor 142,cause the processor to perform the functionality further described abovein conjunction with FIGS. 2 and 5-8 . For example, the memory 144 hasinstructions encoded thereon that, when executed by the processor 142,cause the processor 142 to generate instructions for placing items inregions of an online concierge system-specific portion of a warehouse.As further described below in conjunction with FIGS. 5-8 , the onlineconcierge system 102 accounts for measures of similarity between items,co-occurrences of items in orders, and distances between regions in theonline concierge system-specific portion of the warehouse whendetermining placement of items in the regions of the online conciergesystem-specific portion of the warehouse. Additionally, the onlineconcierge system 102 includes a communication interface configured toconnect the online concierge system 102 to one or more networks, such asnetwork 120, or to otherwise communicate with devices (e.g., clientdevices 110) connected to the one or more networks.

One or more of a client device, a third party system 130, or the onlineconcierge system 102 may be special purpose computing devices configuredto perform specific functions, as further described below in conjunctionwith FIGS. 2-8 , and may include specific computing components such asprocessors, memories, communication interfaces, and/or the like.

System Overview

FIG. 2 illustrates an environment 200 of an online platform, such as anonline concierge system 102, according to one embodiment. The figuresuse like reference numerals to identify like elements. A letter after areference numeral, such as “210 a,” indicates that the text refersspecifically to the element having that particular reference numeral. Areference numeral in the text without a following letter, such as “210,”refers to any or all of the elements in the figures bearing thatreference numeral. For example, “210” in the text refers to referencenumerals “210 a” or “210 b” in the figures.

The environment 200 includes an online concierge system 102. The onlineconcierge system 102 is configured to receive orders from one or moreusers 204 (only one is shown for the sake of simplicity). An orderspecifies a list of goods (items or products) to be delivered to theuser 204. The order also specifies the location to which the goods areto be delivered, and a time window during which the goods should bedelivered. In some embodiments, the order specifies one or moreretailers from which the selected items should be purchased. The usermay use a customer mobile application (CMA) 206 to place the order; theCMA 206 is configured to communicate with the online concierge system102.

The online concierge system 102 is configured to transmit ordersreceived from users 204 to one or more shoppers 208. A shopper 208 maybe a contractor, employee, other person (or entity), robot, or otherautonomous device enabled to fulfill orders received by the onlineconcierge system 202. The shopper 208 travels between a warehouse and adelivery location (e.g., the user's home or office). A shopper 208 maytravel by car, truck, bicycle, scooter, foot, or other mode oftransportation. In some embodiments, the delivery may be partially orfully automated, e.g., using a self-driving car. The environment 200also includes three warehouses 210 a, 210 b, and 210 c (only three areshown for the sake of simplicity; the environment could include hundredsof warehouses). The warehouses 210 may be physical retailers, such asgrocery stores, discount stores, department stores, etc., or non-publicwarehouses storing items that can be collected and delivered to users.Each shopper 208 fulfills an order received from the online conciergesystem 102 at one or more warehouses 210, delivers the order to the user204, or performs both fulfillment and delivery. In one embodiment,shoppers 208 make use of a shopper mobile application 212 which isconfigured to interact with the online concierge system 102.

FIG. 3 is a diagram of an online concierge system 102, according to oneembodiment. In various embodiments, the online concierge system 102 mayinclude different or additional modules than those described inconjunction with FIG. 3 . Further, in some embodiments, the onlineconcierge system 102 includes fewer modules than those described inconjunction with FIG. 3 .

The online concierge system 102 includes an inventory management engine302, which interacts with inventory systems associated with eachwarehouse 210. In one embodiment, the inventory management engine 302requests and receives inventory information maintained by the warehouse210. The inventory of each warehouse 210 is unique and may change overtime. The inventory management engine 302 monitors changes in inventoryfor each participating warehouse 210. The inventory management engine302 is also configured to store inventory records in an inventorydatabase 304. The inventory database 304 may store information inseparate records—one for each participating warehouse 210—or mayconsolidate or combine inventory information into a unified record.Inventory information includes attributes of items that include bothqualitative and qualitative information about items, including size,color, weight, SKU, serial number, and so on. In one embodiment, theinventory database 304 also stores purchasing rules associated with eachitem, if they exist. For example, age-restricted items such as alcoholand tobacco are flagged accordingly in the inventory database 304.Additional inventory information useful for predicting the availabilityof items may also be stored in the inventory database 304. For example,for each item-warehouse combination (a particular item at a particularwarehouse), the inventory database 304 may store a time that the itemwas last found, a time that the item was last not found (a shopperlooked for the item but could not find it), the rate at which the itemis found, and the popularity of the item.

For each item, the inventory database 304 identifies one or moreattributes of the item and corresponding values for each attribute of anitem. For example, the inventory database 304 includes an entry for eachitem offered by a warehouse 210, with an entry for an item including anitem identifier that uniquely identifies the item. The entry includesdifferent fields, with each field corresponding to an attribute of theitem. A field of an entry includes a value for the attributecorresponding to the attribute for the field, allowing the inventorydatabase 304 to maintain values of different categories for variousitems.

In various embodiments, the inventory management engine 302 maintains ataxonomy of items offered for purchase by one or more warehouses 210.For example, the inventory management engine 302 receives an itemcatalog from a warehouse 210 identifying items offered for purchase bythe warehouse 210. From the item catalog, the inventory managementengine 202 determines a taxonomy of items offered by the warehouse 210.different levels in the taxonomy providing different levels ofspecificity about items included in the levels. In various embodiments,the taxonomy identifies a category and associates one or more specificitems with the category. For example, a category identifies “milk,” andthe taxonomy associates identifiers of different milk items (e.g., milkoffered by different brands, milk having one or more differentattributes, etc.), with the category. Thus, the taxonomy maintainsassociations between a category and specific items offered by thewarehouse 210 matching the category. In some embodiments, differentlevels in the taxonomy identify items with differing levels ofspecificity based on any suitable attribute or combination of attributesof the items. For example, different levels of the taxonomy specifydifferent combinations of attributes for items, so items in lower levelsof the hierarchical taxonomy have a greater number of attributes,corresponding to greater specificity in a category, while items inhigher levels of the hierarchical taxonomy have a fewer number ofattributes, corresponding to less specificity in a category. In variousembodiments, higher levels in the taxonomy include less detail aboutitems, so greater numbers of items are included in higher levels (e.g.,higher levels include a greater number of items satisfying a broadercategory). Similarly, lower levels in the taxonomy include greaterdetail about items, so fewer numbers of items are included in the lowerlevels (e.g., higher levels include a fewer number of items satisfying amore specific category). The taxonomy may be received from a warehouse210 in various embodiments. In other embodiments, the inventorymanagement engine 302 applies a trained classification module to an itemcatalog received from a warehouse 210 to include different items inlevels of the taxonomy, so application of the trained classificationmodel associates specific items with categories corresponding to levelswithin the taxonomy.

Inventory information provided by the inventory management engine 302may supplement the training datasets 320. Inventory information providedby the inventory management engine 302 may not necessarily includeinformation about the outcome of picking a delivery order associatedwith the item, whereas the data within the training datasets 320 isstructured to include an outcome of picking a delivery order (e.g., ifthe item in an order was picked or not picked).

The online concierge system 102 also includes an order fulfillmentengine 306 which is configured to synthesize and display an orderinginterface to each user 204 (for example, via the customer mobileapplication 206). The order fulfillment engine 306 is also configured toaccess the inventory database 304 in order to determine which productsare available at which warehouse 210. The order fulfillment engine 306may supplement the product availability information from the inventorydatabase 234 with an item availability predicted by the machine-learneditem availability model 316. The order fulfillment engine 306 determinesa sale price for each item ordered by a user 204. Prices set by theorder fulfillment engine 306 may or may not be identical to in-storeprices determined by retailers (which is the price that users 204 andshoppers 208 would pay at the retail warehouses). The order fulfillmentengine 306 also facilitates transactions associated with each order. Inone embodiment, the order fulfillment engine 306 charges a paymentinstrument associated with a user 204 when he/she places an order. Theorder fulfillment engine 306 may transmit payment information to anexternal payment gateway or payment processor. The order fulfillmentengine 306 stores payment and transactional information associated witheach order in a transaction records database 308.

In various embodiments, the order fulfillment engine 306 generates andtransmits a search interface to a client device of a user for displayvia the customer mobile application 106. The order fulfillment engine306 receives a query comprising one or more terms from a user andretrieves items satisfying the query, such as items having descriptiveinformation matching at least a portion of the query. In variousembodiments, the order fulfillment engine 306 leverages item embeddingsfor items to retrieve items based on a received query. For example, theorder fulfillment engine 306 generates an embedding for a query anddetermines measures of similarity between the embedding for the queryand item embeddings for various items included in the inventory database304.

In some embodiments, the order fulfillment engine 306 also shares orderdetails with warehouses 210. For example, after successful fulfillmentof an order, the order fulfillment engine 306 may transmit a summary ofthe order to the appropriate warehouses 210. The summary may indicatethe items purchased, the total value of the items, and in some cases, anidentity of the shopper 208 and user 204 associated with thetransaction. In one embodiment, the order fulfillment engine 306 pushestransaction and/or order details asynchronously to retailer systems.This may be accomplished via use of webhooks, which enable programmaticor system-driven transmission of information between web applications.In another embodiment, retailer systems may be configured toperiodically poll the order fulfillment engine 306, which providesdetail of all orders which have been processed since the last request.

The order fulfillment engine 306 may interact with a shopper managementengine 310, which manages communication with and utilization of shoppers208. In one embodiment, the shopper management engine 310 receives a neworder from the order fulfillment engine 306. The shopper managementengine 310 identifies the appropriate warehouse 210 to fulfill the orderbased on one or more parameters, such as a probability of itemavailability determined by a machine-learned item availability model316, the contents of the order, the inventory of the warehouses, and theproximity to the delivery location. The shopper management engine e10then identifies one or more appropriate shoppers 208 to fulfill theorder based on one or more parameters, such as the shoppers' proximityto the appropriate warehouse 210 (and/or to the user 204), his/herfamiliarity level with that particular warehouse 210, and so on.Additionally, the shopper management engine 310 accesses a shopperdatabase 312 which stores information describing each shopper 208, suchas his/her name, gender, rating, previous shopping history, and so on.

As part of fulfilling an order, the order fulfillment engine 306 and/orshopper management engine 310 may access a user database 314 whichstores information describing each user. This information could includeeach user's name, address, gender, shopping preferences, favorite items,stored payment instruments, and so on.

In various embodiments, the order fulfillment engine 306 determineswhether to delay display of a received order to shoppers for fulfillmentby a time interval. In response to determining to delay the receivedorder by a time interval, the order fulfilment engine 306 evaluatesorders received after the received order and during the time intervalfor inclusion in one or more batches that also include the receivedorder. After the time interval, the order fulfillment engine 306displays the order to one or more shoppers via the shopper mobileapplication 212; if the order fulfillment engine 306 generated one ormore batches including the received order and one or more ordersreceived after the received order and during the time interval, the oneor more batches are also displayed to one or more shoppers via theshopper mobile application 212.

Additionally, the order fulfillment engine 306 includes one or moremodels that generate an affinity graph between items offered by awarehouse 210 based on co-occurrences of items in previously receivedorders. The models determine a measure of affinity between items, suchas a co-occurrence score based on a number of orders including both ofthe items and generates an affinity graph where a connection betweenitems has a weight of the affinity score between the items. The affinitygraph is further described below in conjunction with FIGS. 5 and 6 .

Additionally, the order fulfillment engine 306 generates a colocationgraph describing distances between regions in an online conciergesystem-specific portion of a warehouse 210. As further described belowin conjunction with FIG. 5 , the online concierge system-specificportion of the warehouse 210 is a location within the warehouse 210where the order fulfillment engine 306 determines placement of items indifferent regions. This allows the order fulfillment engine 306 toleverage information about previously fulfilled orders to place itemswithin the online concierge system-specific portion of the warehouse 210to reduce amounts of time for shoppers to obtain items for fulfillingorders. The colocation graph identifies different regions within theonline concierge system-specific portion of the warehouse 210, with aconnection between a pair of regions having a weight specifying adistance between the regions of the pair. A colocation graph is furtherdescribed below in conjunction with FIGS. 5 and 7 .

From the colocation graph and the affinity graph, the order fulfillmentengine 306 evaluates different combinations of pairs matching items withregions within the online concierge system-specific portion of thewarehouse 210. As further described below in conjunction with FIG. 5 ,the order fulfillment engine 306 accounts for co-occurrences of items inorders, measures of similarity between items, distances between regionswithin the online concierge system-specific region of the warehouse 210and predicted numbers of orders including items when evaluating pairs ofitems and regions within the online concierge system-specific portion ofthe warehouse 210. For example, the order fulfillment engine 306 appliesan optimization function to combinations of pairs of an item and aregion within the online concierge system-specific portion of thewarehouse 210, with the optimization function generating a value for acombination. Based on the values for different combinations, the orderfulfillment engine 306 selects pairs of an item and a region withinonline concierge system-specific portion of the warehouse 210. From theselected pairs, the order fulfillment engine 306 generates instructionsthat are transmitted to the warehouse 210 for placing items in regionswithin the online concierge system-specific portion of the warehouse210, as further described below in conjunction with FIGS. 5 and 8 . Thisallows the order fulfillment engine 306 to have items placed in regionswithin the online concierge system-specific portion of the warehouse 210that reduce an amount of time for shoppers to obtain items forfulfilling an order from the warehouse 210.

Machine Learning Models

The online concierge system 102 further includes a machine-learned itemavailability model 316, a modeling engine 318, and training datasets320. The modeling engine 318 uses the training datasets 320 to generatethe machine-learned item availability model 316. The machine-learneditem availability model 316 can learn from the training datasets 320,rather than follow only explicitly programmed instructions. Theinventory management engine 302, order fulfillment engine 306, and/orshopper management engine 310 can use the machine-learned itemavailability model 316 to determine a probability that an item isavailable at a warehouse 210. The machine-learned item availabilitymodel 316 may be used to predict item availability for items beingdisplayed to or selected by a user or included in received deliveryorders. A single machine-learned item availability model 316 is used topredict the availability of any number of items.

The machine-learned item availability model 316 can be configured toreceive as inputs information about an item, the warehouse for pickingthe item, and the time for picking the item. The machine-learned itemavailability model 316 may be adapted to receive any information thatthe modeling engine 318 identifies as indicators of item availability.At minimum, the machine-learned item availability model 316 receivesinformation about an item-warehouse pair, such as an item in a deliveryorder and a warehouse at which the order could be fulfilled. Itemsstored in the inventory database 304 may be identified by itemidentifiers. As described above, various characteristics, some of whichare specific to the warehouse (e.g., a time that the item was last foundin the warehouse, a time that the item was last not found in thewarehouse, the rate at which the item is found, the popularity of theitem) may be stored for each item in the inventory database 304.Similarly, each warehouse may be identified by a warehouse identifierand stored in a warehouse database along with information about thewarehouse. A particular item at a particular warehouse may be identifiedusing an item identifier and a warehouse identifier. In otherembodiments, the item identifier refers to a particular item at aparticular warehouse, so that the same item at two different warehousesis associated with two different identifiers. For convenience, both ofthese options to identify an item at a warehouse are referred to hereinas an “item-warehouse pair.” Based on the identifier(s), the onlineconcierge system 102 can extract information about the item and/orwarehouse from the inventory database 304 and/or warehouse database andprovide this extracted information as inputs to the item availabilitymodel 316.

The machine-learned item availability model 316 contains a set offunctions generated by the modeling engine 318 from the trainingdatasets 320 that relate the item, warehouse, and timing information,and/or any other relevant inputs, to the probability that the item isavailable at a warehouse. Thus, for a given item-warehouse pair, themachine-learned item availability model 316 outputs a probability thatthe item is available at the warehouse. The machine-learned itemavailability model 316 constructs the relationship between the inputitem-warehouse pair, timing, and/or any other inputs and theavailability probability (also referred to as “availability”) that isgeneric enough to apply to any number of different item-warehouse pairs.In some embodiments, the probability output by the machine-learned itemavailability model 316 includes a confidence score. The confidence scoremay be the error or uncertainty score of the output availabilityprobability and may be calculated using any standard statistical errormeasurement. In some examples, the confidence score is based in part onwhether the item-warehouse pair availability prediction was accurate forprevious delivery orders (e.g., if the item was predicted to beavailable at the warehouse and not found by the shopper or predicted tobe unavailable but found by the shopper). In some examples, theconfidence score is based in part on the age of the data for the item,e.g., if availability information has been received within the pasthour, or the past day. The set of functions of the item availabilitymodel 316 may be updated and adapted following retraining with newtraining datasets 320. The machine-learned item availability model 316may be any machine learning model, such as a neural network, boostedtree, gradient boosted tree or random forest model. In some examples,the machine-learned item availability model 316 is generated fromXGBoost algorithm.

The item probability generated by the machine-learned item availabilitymodel 316 may be used to determine instructions delivered to the user204 and/or shopper 208, as described in further detail below.

The training datasets 320 relate a variety of different factors to knownitem availabilities from the outcomes of previous delivery orders (e.g.,if an item was previously found or previously unavailable). The trainingdatasets 320 include the items included in previous delivery orders,whether the items in the previous delivery orders were picked,warehouses associated with the previous delivery orders, and a varietyof characteristics associated with each of the items (which may beobtained from the inventory database 204). Each piece of data in thetraining datasets 320 includes the outcome of a previous delivery order(e.g., if the item was picked or not). The item characteristics may bedetermined by the machine-learned item availability model 316 to bestatistically significant factors predictive of the item's availability.For different items, the item characteristics that are predictors ofavailability may be different. For example, an item type factor might bethe best predictor of availability for dairy items, whereas a time ofday may be the best predictive factor of availability for vegetables.For each item, the machine-learned item availability model 316 mayweight these factors differently, where the weights are a result of a“learning” or training process on the training datasets 320. Thetraining datasets 320 are very large datasets taken across a wide crosssection of warehouses, shoppers, items, warehouses, delivery orders,times, and item characteristics. The training datasets 320 are largeenough to provide a mapping from an item in an order to a probabilitythat the item is available at a warehouse. In addition to previousdelivery orders, the training datasets 320 may be supplemented byinventory information provided by the inventory management engine 302.In some examples, the training datasets 320 are historic delivery orderinformation used to train the machine-learned item availability model316, whereas the inventory information stored in the inventory database304 include factors input into the machine-learned item availabilitymodel 316 to determine an item availability for an item in a newlyreceived delivery order. In some examples, the modeling engine 318 mayevaluate the training datasets 320 to compare a single item'savailability across multiple warehouses to determine if an item ischronically unavailable. This may indicate that an item is no longermanufactured. The modeling engine 318 may query a warehouse 210 throughthe inventory management engine 302 for updated item information onthese identified items.

Machine Learning Factors

The training datasets 320 include a time associated with previousdelivery orders. In some embodiments, the training datasets 320 includea time of day at which each previous delivery order was placed. Time ofday may impact item availability, since during high-volume shoppingtimes, items may become unavailable that are otherwise regularly stockedby warehouses. In addition, availability may be affected by restockingschedules, e.g., if a warehouse mainly restocks at night, itemavailability at the warehouse will tend to decrease over the course ofthe day. Additionally, or alternatively, the training datasets 320include a day of the week previous delivery orders were placed. The dayof the week may impact item availability since popular shopping days mayhave reduced inventory of items or restocking shipments may be receivedon particular days. In some embodiments, training datasets 320 include atime interval since an item was previously picked in a previouslydelivery order. If an item has recently been picked at a warehouse, thismay increase the probability that it is still available. If there hasbeen a long time interval since an item has been picked, this mayindicate that the probability that it is available for subsequent ordersis low or uncertain. In some embodiments, training datasets 320 includea time interval since an item was not found in a previous deliveryorder. If there has been a short time interval since an item was notfound, this may indicate that there is a low probability that the itemis available in subsequent delivery orders. And conversely, if there ishas been a long time interval since an item was not found, this mayindicate that the item may have been restocked and is available forsubsequent delivery orders. In some examples, training datasets 320 mayalso include a rate at which an item is typically found by a shopper ata warehouse, a number of days since inventory information about the itemwas last received from the inventory management engine 302, a number oftimes an item was not found in a previous week, or any number ofadditional rate or time information. The relationships between this timeinformation and item availability are determined by the modeling engine318 training a machine learning model with the training datasets 320,producing the machine-learned item availability model 316.

The training datasets 320 include item characteristics. In someexamples, the item characteristics include a department associated withthe item. For example, if the item is yogurt, it is associated with thedairy department. The department may be the bakery, beverage, nonfood,and pharmacy, produce and floral, deli, prepared foods, meat, seafood,dairy, the meat department, or dairy department, or any othercategorization of items used by the warehouse. The department associatedwith an item may affect item availability, since different departmentshave different item turnover rates and inventory levels. In someexamples, the item characteristics include an aisle of the warehouseassociated with the item. The aisle of the warehouse may affect itemavailability since different aisles of a warehouse may be morefrequently re-stocked than others. Additionally, or alternatively, theitem characteristics include an item popularity score. The itempopularity score for an item may be proportional to the number ofdelivery orders received that include the item. An alternative oradditional item popularity score may be provided by a retailer throughthe inventory management engine 302. In some examples, the itemcharacteristics include a product type associated with the item. Forexample, if the item is a particular brand of a product, then theproduct type will be a generic description of the product type, such as“milk” or “eggs.” The product type may affect the item availability,since certain product types may have a higher turnover and re-stockingrate than others or may have larger inventories in the warehouses. Insome examples, the item characteristics may include a number of times ashopper was instructed to keep looking for the item after he or she wasinitially unable to find the item, a total number of delivery ordersreceived for the item, whether or not the product is organic, vegan,gluten free, or any other characteristics associated with an item. Therelationships between item characteristics and item availability aredetermined by the modeling engine 318 training a machine learning modelwith the training datasets 320, producing the machine-learned itemavailability model 316.

The training datasets 320 may include additional item characteristicsthat affect the item availability and can therefore be used to build themachine-learned item availability model 316 relating the delivery orderfor an item to its predicted availability. The training datasets 320 maybe periodically updated with recent previous delivery orders. Thetraining datasets 320 may be updated with item availability informationprovided directly from shoppers 208. Following updating of the trainingdatasets 320, a modeling engine 318 may retrain a model with the updatedtraining datasets 320 and produce a new machine-learned itemavailability model 316.

Customer Mobile Application

FIG. 4A is a diagram of the customer mobile application (CMA) 206,according to one embodiment. The CMA 206 includes an ordering interface402, which provides an interactive interface with which the user 104 canbrowse through and select products and place an order. The CMA 206 alsoincludes a system communication interface 404 which, among otherfunctions, receives inventory information from the online shoppingconcierge system 102 and transmits order information to the system 202.The CMA 206 also includes a preferences management interface 406 whichallows the user 104 to manage basic information associated with his/heraccount, such as his/her home address and payment instruments. Thepreferences management interface 406 may also allow the user to manageother details such as his/her favorite or preferred warehouses 210,preferred delivery times, special instructions for delivery, and so on.

Shopper Mobile Application

FIG. 4B is a diagram of the shopper mobile application (SMA) 212,according to one embodiment. The SMA 212 includes a barcode scanningmodule 420 which allows a shopper 208 to scan an item at a warehouse 210(such as a can of soup on the shelf at a grocery store). The barcodescanning module 420 may also include an interface which allows theshopper 108 to manually enter information describing an item (such asits serial number, SKU, quantity and/or weight) if a barcode is notavailable to be scanned. SMA 212 also includes a basket manager 422which maintains a running record of items collected by the shopper 208for purchase at a warehouse 210. This running record of items iscommonly known as a “basket.” In one embodiment, the barcode scanningmodule 420 transmits information describing each item (such as its cost,quantity, weight, etc.) to the basket manager 422, which updates itsbasket accordingly. The SMA 212 also includes a system communicationinterface 424 which interacts with the online shopping concierge system102. For example, the system communication interface 424 receives anorder from the online concierge system 102 and transmits the contents ofa basket of items to the online concierge system 102. The SMA 212 alsoincludes an image encoder 426 which encodes the contents of a basketinto an image. For example, the image encoder 426 may encode a basket ofgoods (with an identification of each item) into a QR code which canthen be scanned by an employee of the warehouse 210 at check-out.

Generating Placement of Items in Regions of a Specific Portion of aWarehouse from Prior Orders Fulfilled by an Online Concierge System

FIG. 5 is a flowchart of one embodiment of a method for an onlineconcierge system 102 arranging items in an online conciergesystem-specific portion of a warehouse. In various embodiments, themethod includes different or additional steps than those described inconjunction with FIG. 5 . Further, in some embodiments, the steps of themethod may be performed in different orders than the order described inconjunction with FIG. 5 . The method described in conjunction with FIG.5 may be carried out by the online concierge system 102 in variousembodiments, while in other embodiments, the steps of the method areperformed by any online system capable of retrieving items.

To simplify retrieval of items for orders, the online concierge system102 and a warehouse 210 establish an online concierge system-specificportion of the warehouse 210. As further described below, the onlineconcierge system 102 provides the warehouse 210 with instructions forarranging items within the online concierge system-specific portion ofthe warehouse 210. This allows the online concierge system 102 toarrange items in the online concierge system-specific portion of thewarehouse 210 to reduce amounts of time for shoppers to obtain ordersfrom the online concierge system-specific portion of the warehouse 210,decreasing fulfillment time for orders received by the online conciergesystem 102 that identify the warehouse 210. In various embodiments, theonline concierge system-specific portion of the warehouse 210 comprisesan aisle, or multiple aisles, within the warehouse 210. Further, thewarehouse 210 may determine a size of the online conciergesystem-specific portion of the warehouse 210 as a percentage of the areawithin the warehouse; for example, the online concierge system-specificportion of the warehouse 210 occupies 15% or 20% of the area within thewarehouse 210.

In various embodiments, the online concierge system-specific portion ofthe warehouse 210 is segmented into multiple regions by the onlineconcierge system 102, with each region uniquely identified by a regionidentifier. For example, a region identifier includes an aisleidentifier of an aisle within the online concierge system-specificportion of the warehouse 210, a row identifier specifying a row on theaisle corresponding to the aisle identifier, and a column identifierspecifying a column of the aisle corresponding to the aisle identifier.However, in other embodiments, the online concierge system 102 usesother information to identify different regions within the onlineconcierge system-specific portion of the warehouse 210.

To arrange items in regions within the online concierge system-specificportion of the warehouse 210, the online concierge system 102 retrieves505 orders previously fulfilled by the online concierge system 102 viathe warehouse 210. From the previously fulfilled orders, the onlineconcierge system 102 generates 510 an affinity graph comprising itemsoffered by the warehouse 210 and connections between items. In variousembodiments, the online concierge system 102 selects a set of candidateitems and generates 510 the affinity graph including the candidate itemsand connections between the candidate items. In some embodiments, theonline concierge system 102 selects the set of candidate items based onpredicted numbers of orders including different items. For example, theonline concierge system 102 applies a trained prediction model to itemsoffered by a warehouse 210 to determine a predicted number of ordersincluding an item. The prediction model may be trained from examplescomprising different time intervals labeled with numbers of ordersreceived during a time interval including the item. In variousembodiments, the prediction model is a classical time series model,while in other embodiments the prediction model is a neural networktrained by backpropagation of an error term based on a loss functionbetween a label applied to an example and a predicted number of ordersthrough layers of the neural network until one or more conditions aresatisfied. The online concierge system 102 may apply the trainedprediction model to numbers of orders including an item during timeintervals satisfying particular criteria (e.g., occurring within athreshold amount of time from a current time interval, occurring withinthe same month or day of the week as a current time interval, etc.).

The online concierge system 102 may account for rates, or predictedrates, at which items are found by shoppers when selecting candidateitems. For example, the rate at which an item has been found isdetermined by dividing the number of previously fulfilled ordersincluding the item in which a shopper obtained the item by the number ofpreviously received orders including the item. In various embodiments,the rate at which an item has been found is determined for a particulartime interval or determined from previously received orders includingthe item obtained (or fulfilled) that satisfy one or more criteria. Insome embodiments, the online concierge system determines a rate at whichan item was not found as the inverse of the rate at which the item hasbeen found (e.g., the rate at which the item was not found is determinedby subtracting the rate at which the item was found from 1) and uses therate at which the item was not found to select the candidate items. Forexample, the online concierge system 102 generates a score for each itemoffered by the warehouse 210, with the score for an item a product of apredicted number of offers including the item and the rate at which theitem was not found. The online concierge system 102 may rank the itemsoffered by the warehouse 210 based on their scores and select the set ofcandidate items based on the ranking. For example, the online conciergesystem 102 determines a percentage of area of the warehouse 210allocated for the online concierge system-specific portion of thewarehouse 210, determines positions in the ranking corresponding to thedetermined percentage, and selects the set of candidate items as itemsin positions of the ranking corresponding to the determined positions.

The online concierge system 102 determines an affinity score between anitem and an additional item based on characteristics of the item and theadditional item. For example, an affinity score between the item and theadditional item is based on a co-occurrence score for the item and theadditional item. The co-occurrence score between an item and anadditional item is based on a number of previously fulfilled ordersincluding the item and the additional item. In an example, the onlineconcierge system 102 determines the co-occurrence score between the itemand the additional item by determining a number of previously fulfilledorders including both the item and the additional item and determining asum of a number of items including the item and a number of itemsincluding the additional item. The co-occurrence score between the itemand the additional item is determined by dividing the number of ordersincluding both the item and the additional item by the sum. In someembodiments, the online concierge system 102 determines theco-occurrence score between the item and the additional item bymultiplying the number of previously fulfilled orders including the itemand including the additional item by a constant (e.g., 2) and dividingthe resulting product by a sum of a number of items including the itemand a number of items including the additional item. In someembodiments, the affinity score between the item and the additional itemis the co-occurrence score of the item and the additional item.

The affinity score between the item and the additional item may accountfor other characteristics between the item and the additional item inother embodiments. For example, the online concierge system 102determines a measure of similarity between the item and the additionalitem and uses the measure of similarity when determining the affinityscore between the item and the additional item. In various embodiments,the online concierge system 102 determines item embeddings for each itemoffered by a warehouse 210, and the measure of similarity between anitem and an additional item is a measure of similarity between an itemembedding for the item and an additional item embedding for theadditional item. Example measures of similarity between item embeddingsinclude a cosine similarity or a dot product between the itemembeddings. In other embodiments, the online concierge system 102determines the measure of similarity between the item and the additionalitem based on a number of common attributes of the item and theadditional item. The online concierge system 102 may determine theaffinity score between the item and the additional item by combining theco-occurrence score of the item and the additional item and the measureof similarity between the item and the additional item. The affinityscore between the item and the additional item is stored as a weight ofa connection between the item and the additional item when generating510 the affinity graph. In other embodiments, the online conciergesystem 102 stores the co-occurrence score and the measure of similarityas weights of a connection between the item and the additional item whengenerating 510 the affinity graph.

FIG. 6 shows an example affinity graph 600 generated by the onlineconcierge system 102. The example affinity graph 600 includes nodescorresponding to item 605, item 610, item 615, item 620, and item 625.As further described above in conjunction with FIG. 5 , the exampleaffinity graph 600 also includes connections between pairs of items,with each connection having an affinity score as a weight, with theaffinity score based on a number of items including both items in apair. In the example of FIG. 6 , connection 630 between item 605 anditem 610 has a weight of affinity score 660, which is based on a numberof orders from a warehouse 210 including item 605 and item 610.Similarly, connection 635 between item 605 and item 615 has a weight ofaffinity score 665, based on co-occurrences of item 605 and item 615 inorders identifying the warehouse 210. Item 605 and item 620 areconnected by connection 640, to which affinity score 670, determinedfrom orders including both item 605 and item 620, is applied. Similarly,item 610 and item 625 are connected by connection 645, having a weightof affinity score 675. Connection 650 connects item 615 to item 625 witha weight of affinity score 680, while connection 655 connects item 625and item 620 with a weight of affinity score 685.

In various embodiments, an affinity score of a connection between afirst item and a second item is a co-occurrence score calculated bydividing a number of received orders including both the first item andthe second item by a sum of received orders that include the first itemand received orders that include the second item. However, in otherembodiments, the affinity score of the connection may account foradditional information. For example, the affinity score between a firstitem and a second item may be based on both the co-occurrence score ofthe first item and the second item and a measure of similarity betweenthe first item and the second item; in the preceding example, theaffinity score between the first item and the second item may beinversely related to the measure of similarity, resulting a lowermeasure of similarity score increasing the affinity score. In otherembodiments, the connection may include multiple weights. For example, aconnection between the first item and the second item includes aco-occurrence score for the first item and the second item and a measureof similarity between the first item and the second item.

Referring back to FIG. 5 , the online concierge system 102 generates 515clusters of items. In various embodiments, the online concierge system102 uses hierarchical clustering to generate 515 the clusters of items.The online concierge system 102 generates 515 clusters so items includedin a cluster have maximum co-occurrence scores with each other andminimum measures of similarity with each other in various embodiments.The online concierge system 102 generates 515 clusters of items based ondistances between items, with clusters including items having less thana threshold distance between each other in the affinity graph orincluding items so distances between items in the cluster is minimized.To generate 515 clusters, the online concierge system 102 determinesdistances between pairs of items using a measure of similarity betweenthe items of the pair and the co-occurrence score between the items ofthe pair. For example, the online concierge system 102 determines adistance between an item and an additional item by dividing the measureof similarity between the item and the additional item by theco-occurrence score of the item and the additional item. Using thedetermined distances between items, the online concierge system 102applies one or more clustering models to generate 515 clusters. Invarious embodiments, using hierarchical clustering identifies a seriesof hierarchical groups for each item included in the affinity graph. Invarious embodiments, different groups of the series correspond todifferent levels in a taxonomy maintained by the online concierge system102, as further described above in conjunction with FIG. 3 .

While the affinity graph and the clusters of items allows the onlineconcierge system 102 to identify relationships between items andgenerate 515 clusters of items with high co-occurrence scores and lowmeasures of similarities, the online concierge system 102 also generates520 a colocation graph describing a physical layout of the onlineconcierge system-specific portion of the warehouse 210. In variousembodiments, the colocation graph allows the online concierge system 102to account for distances between different regions of the onlineconcierge system-specific portion of the warehouse 210, which allows theonline concierge system 102 to account for travel time between regionsof the online concierge system-specific portion of the warehouse 210 forshoppers. The colocation graph identifies each region within the onlineconcierge system-specific portion of the warehouse 210 and maintainsconnections between different pairs of regions within online conciergesystem-specific portion of the warehouse 210. A weight of a connectionbetween a region within the online concierge system-specific portion ofthe warehouse 210 and an additional region within the online conciergesystem-specific portion of the warehouse 210 is a distance between theregion and the additional region.

In various embodiments, the online concierge system 102 determines adistance between a region within the online concierge system-specificportion of the warehouse 210 and an additional region within the onlineconcierge system-specific portion of the warehouse 210 by combiningdistances between different coordinates identifying the region and theadditional region. For example, a region is identified by a combinationof an aisle identifier, a row identifier, and a column identifier, asfurther described above. Similarly, an additional region is identifiedby a combination of an additional aisle identifier, an additional rowidentifier, and an additional column identifier. The online conciergesystem 102 determines an aisle distance between the aisle identifier andthe additional aisle identifier, a row distance between the rowidentifier and the additional row identifier, and a column distancebetween the column identifier and the additional column identifier. Todetermine the distance between the region and the additional region, theonline concierge system 102 combines the aisle distance, the rowdistance, and the column distance. For example, the distance between theregion and the additional region is a sum of the aisle distance, the rowdistance, and the column distance. In other embodiments, differentweights are applied to the aisle distance, to the row distance, and tothe column distance, with the distance between the region and theadditional region a weighted sum of the aisle distance, the rowdistance, and the column distance. Weights applied to the aisledistance, to the row distance, and to the column distance may bedifferent for different warehouses 210. In some embodiments, the onlineconcierge system stores weights for application to the aisle distance,to the row distance, and to the column distance in association withwarehouse identifiers for different warehouses 210.

FIG. 7 is an example colocation graph 700 generated by the onlineconcierge system 102. For purposes of illustration, the examplecolocation graph 700 identifies three regions—region 705, region 710,region 725—in an online concierge system-specific portion of a warehouse210. As further described above in conjunction with FIG. 5 , the onlineconcierge system 102 determines a distance between each pair of regions.In some embodiments, the distance between a pair of regions isdetermined from a combination of distances between different coordinatesidentifying regions within the online concierge system-specific portionof the warehouse 210.

Using the distances between different pairs of regions, the onlineconcierge system 102 generates the example colocation graph 700. Eachnode in the example colocation graph 700 corresponds to a region withinthe online concierge system-specific region of the warehouse 210. Aconnection between a region and an additional region in the examplecolocation graph 700 has a weight specifying a distance between theregion and the additional region. In the example colocation graph 700 ofFIG. 7 , connection 715 between region 705 and region 710 specifiesdistance 720 between region 705 and region 710, while connection 730between region 705 and region 725 specifies distance 730 between region705 and region 725. Similarly, connection 745 between region 725 andregion 710 specifies distance 750 between region 725 and region 710.Hence, the example colocation graph 700 identifies distances betweeneach pair of regions in the online concierge system-specific portion ofthe warehouse 210. In various embodiments, the example colocation graph700 is stored in association with an identifier of the warehouse 210,allowing the online concierge system 102 to maintain differentcolocation graphs for different warehouses 210.

Referring back to FIG. 5 , the online concierge system 102 leverages theaffinity graph and the colocation graph to determine placement of itemsin regions of the online concierge system-specific portion of thewarehouse 210. Using the affinity graph and the colocation graph allowsthe online concierge system 102 to generate 525 instructions for placingitems in different regions of the online concierge system-specificportion of the warehouse 210 that reduces amounts of time for shoppersto retrieve items from the warehouse 210 when fulfilling orders. Invarious embodiments, the online concierge system 102 applies one or moregreedy optimization methods to combinations of pairs of items andregions within the online concierge system-specific portion of thewarehouse 210 subject to an optimization function. In variousembodiments, the optimization function accounts for distances betweenregions within the online concierge system-specific portion of thewarehouse 210, co-occurrences of items, measures of similarity betweenitems, predicted numbers of orders including items, and rates at whichitems have been found by shoppers. In various embodiments, the onlineconcierge system 102 applies a trained prediction model to numbers oforders including an item at different times to predict a number oforders including the item. The prediction model may be trained fromexamples comprising different time intervals labeled with numbers oforders received during a time interval including the item. In variousembodiments, the prediction model is a classical time series model,while in other embodiments the prediction model is a neural networktrained by backpropagation of an error term based on a loss functionbetween a label applied to an example and a predicted number of ordersthrough layers of the neural network until one or more conditions aresatisfied. Further, a rate at which an item has been found by shoppersis based on a previously received number of orders including the itemand a number of previously fulfilled orders including the item in whicha shopper obtained the item. For example, the rate at which the item hasbeen found is determined by dividing the number of previously fulfilledorders including the item in which a shopper obtained the item by thenumber of previously received orders including the item. In variousembodiments, the rate at which an item has been found is determined fora particular time interval or determined from previously received ordersincluding the item obtained (or fulfilled) that satisfy one or morecriteria.

For a combination of an item at a region within online conciergesystem-specific portion of the warehouse 210 and an additional item atan additional region within the online concierge system-specific portionof the warehouse 210, the online concierge system 102 determines aco-occurrence value as a product of a distance between the region andthe additional region, the co-occurrence score of the item and theadditional item, a predicted number of orders including the item, and apredicted number of orders including the additional item. The onlineconcierge system 102 also determines a similarity value by multiplyingthe measure of similarity between the item and the additional item, apredicted number of orders in which the item was found, and a predictednumber of orders in which the additional item was found. In thepreceding example, the online concierge system 102 determines theoptimization function for the combination by dividing the co-occurrencevalue by the similarity value. In various embodiments, the onlineconcierge system 102 applies the optimization function to eachcombination of pairs of items and regions within the online conciergesystem-specific portion of the warehouse 210. For example, the onlineconcierge system 102 selects a cluster of items, generates each pair ofitem of the cluster and region of the online concierge system-specificportion of the warehouse 210, and applies the optimization function toeach combination of two pairs of item of the cluster and region of theonline concierge system-specific portion of the warehouse 210. Inembodiments where the items are hierarchically clustered, the onlineconcierge system 102 selects a cluster at a lowest level in thehierarchy and applies the optimization function to each combination oftwo pairs of regions within the online concierge system-specific regionof the warehouse 210 and an item of the cluster, in various embodiments.Selecting a cluster to which the optimization function is applied allowsthe online concierge system 102 to more efficiently allocatecomputational resources for application of the optimization function.

The online concierge system 102 selects combinations of pairs of itemsand regions within the online concierge system-specific portion of thewarehouse 210 based on the values for the combinations from theoptimization function. A selected combination of pairs includes a firstpair of an item and a region within the online concierge system-specificportion of the warehouse 210 and a second pair of an additional item andan additional region within online concierge system-specific portion ofthe warehouse 210. Hence, a selected combination corresponds toplacement of an item at a region within the online conciergesystem-specific portion of the warehouse 210 and of an additional itemat an additional region within the online concierge system-specificportion of the warehouse 210. The online concierge system 102 generates525 the instructions for placing items within the online conciergesystem-specific portion of the warehouse 210 from the selectedcombinations. For example, the instructions include an item identifierand a corresponding identifier of a region within the online conciergesystem-specific portion of the warehouse 210 to specify placement of anitem corresponding to the item identifier at the region corresponding tothe identifier of the region within the online concierge system-specificportion of the warehouse 210.

FIG. 8 is a process flow diagram of one embodiment of placement of itemswithin regions of an online concierge system-specific portion of awarehouse 210 using an optimization process. This optimization processreceives as inputs a list of regions and a list of items. For purposesof illustration, FIG. 8 shows three regions—region 705, region 710,region 715—and three items—item 805, item 810, item 815. To determineplacement of items in regions of the online concierge system-specificportion of the warehouse 210, the online concierge system 102 generatespairs that each include an item and a region. For purposes ofillustration, FIG. 8 shows a pair including region 705 and item 805, apair including region 710 and item 810, a pair including region 725 anditem 815, a pair including region 705 and item 810, and a pair includingregion 710 and item 815. However, in various embodiments, the onlineconcierge system 102 generates a pair corresponding to each combinationof region and item.

The online concierge system 102 generates combinations each includingtwo generated pairs in various embodiments. In the example of FIG. 8 ,combination 820 includes the pair including region 705 and item 805 andthe pair including region 710 and item 810. Similarly, combination 825in FIG. 8 includes the pair including region 705 and item 805 and thepair including region 725 and item 815. FIG. 8 also shows combination830 including the pair including region 750 and item 810 and the pairincluding region 710 and item 815. In various embodiments, the onlineconcierge system 102 generates a combination corresponding to eachgrouping of two generated pairs.

The online concierge system 102 applies an optimization function 835 toeach of combination 820, combination 825, and combination 830. Theoptimization function 835 outputs a cost 840 (or score) for each of thecombinations 820, 825, 830. As further described above in conjunctionwith FIG. 5 , the optimization function 835 accounts for distancesbetween regions within the online concierge system-specific portion ofthe warehouse 210, co-occurrences of items, measures of similaritybetween items, predicted numbers of orders including items, and rates atwhich items have been found by shoppers. Application of the optimizationfunction 835 to a combination results in a value for a combination. Inthe embodiment shown by FIG. 8 , the online concierge system 102 ranksthe combinations based on their corresponding scores 840 from theoptimization function 835, thereby providing a ranking of combination830, combination 820, and combination 825. The online concierge system102 selects one or more combinations based on the ranking and generatesinstructions for placing items in regions of the online conciergesystem-specific portion of the warehouse 210 based on the pairs of itemand region in the selected one or more combinations.

Referring back to FIG. 5 , the online concierge system 102 transmits 530the instructions for placing items within regions of online conciergesystem-specific portion of the warehouse 210 to a client device 110 orto another computing device associated with the warehouse 210. Based onthe instructions, the warehouse 210 places items in regions of theonline concierge system-specific portion of the warehouse 210. As theinstructions for placing items in regions of the online conciergesystem-specific portion of the warehouse 210 specify physical locationsof items in the online concierge system-specific portion of thewarehouse 210, generating 525 the instructions from the optimizationfunction accounting for characteristics of items and of regions withinthe online concierge system-specific portion of the warehouse 210 allowsitems to be placed in regions that limits an amount of time (or anamount of distance) for a shopper to retrieve items from the onlineconcierge system-specific portion of the warehouse 210.

After transmitting 530 instructions for placing items within regions ofthe online concierge system-specific portion of the warehouse 210, whenthe online concierge system 102 receives a selection of an order by ashopper to fulfill an order at the warehouse 210, the online conciergesystem 102 transmits instructions to a client device 110 of the shopperfor obtaining items included in the order from within the warehouse 210.The transmitted instructions identify an item included in the onlineconcierge system-specific portion of the warehouse 210 and acorresponding region of the online concierge system-specific portion ofthe warehouse 210 from which the item is obtained. As the items in theonline concierge system-specific portion of the warehouse 210 are placedby the online concierge system 102 to minimize an amount of time or adistance traveled by a shopper to retrieve the items, the shopper isable to more rapidly acquire items from the online conciergesystem-specific portion of the warehouse 210.

Additionally, when a shopper obtains an item from the online conciergesystem-specific portion of the warehouse 210, the shopper captures animage of the region of the online concierge system-specific portion ofthe warehouse 210 from which the item was obtained via a client device110. The online concierge system 102 receives the image from the clientdevice 110. The online concierge system 102 applies one or more imageprocessing methods to determine one or more regions of the onlineconcierge system-specific portion of the warehouse 210 in the image andidentifies items included in the one or more regions of the onlineconcierge system-specific portion of the warehouse 210 in the image. Asthe online concierge system 102 determined which items were placed invarious regions within the online concierge system-specific portion ofthe warehouse 210, analyzing the regions of the online conciergesystem-specific portion of the warehouse 210 allows the online conciergesystem 102 to determine inventory levels of items placed in differentregions of the online concierge system-specific portion of the warehouse210. In various embodiments, the online concierge system 102 updates themachine-learned item availability model 316 based on inventory of itemsdetermined from the received information, allowing the online conciergesystem 102 to improve accuracy of the machine-learned item availabilitymodel 316 by providing more frequent information to the online conciergesystem 102 about inventory of items within the online conciergesystem-specific portion of the warehouse 210. For example, the onlineconcierge system 102 generates an example including an itemcorresponding to a region within the online concierge-system specificportion of the warehouse 210 including an identifier of the item andinformation about the warehouse 210 and applies a label to the exampleindicating whether the item was available at the warehouse 210 based onthe image. The online concierge system 102 applies the machine-learneditem availability model 316 to the example and generates an error termby applying a loss function to a difference between the label applied tothe example and a predicted availability output by the machine-learneditem availability model 316. The online concierge system 102backpropagates the error term through the machine-learned itemavailability model 316 to modify one or more parameters of themachine-learned item availability model 316.

Additional Considerations

The foregoing description of the embodiments of the disclosure has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the disclosure to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of thedisclosure in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the disclosure may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a tangible computer readable storage medium, whichinclude any type of tangible media suitable for storing electronicinstructions and coupled to a computer system bus. Furthermore, anycomputing systems referred to in the specification may include a singleprocessor or may be architectures employing multiple processor designsfor increased computing capability.

Embodiments of the disclosure may also relate to a computer data signalembodied in a carrier wave, where the computer data signal includes anyembodiment of a computer program product or other data combinationdescribed herein. The computer data signal is a product that ispresented in a tangible medium or carrier wave and modulated orotherwise encoded in the carrier wave, which is tangible, andtransmitted according to any suitable transmission method.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the disclosure be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the disclosure is intended to be illustrative, but not limiting, ofthe scope of the disclosure, which is set forth in the following claims.

What is claimed is:
 1. A method comprising: generating, by an onlineconcierge system, an affinity graph including nodes representing itemsoffered by a warehouse and connections between pairs of items offered bythe warehouse, wherein a connection between a pair of items isdetermined based on co-occurrences of items of the pair occurring inpreviously received orders identifying the warehouse; generating acolocation graph for the warehouse, the colocation graph correspondingto an online concierge system-specific portion of the warehouse andincluding different regions within the online concierge system-specificportion of the warehouse, wherein the colocation graph includesconnections between pairs of regions within the online conciergesystem-specific portion of the warehouse, and wherein the connectionsare determined based on distances between regions within the onlineconcierge system-specific portion of the warehouse; generating, by theonline concierge system, instructions for placing items in regionswithin the online concierge system-specific portion of the warehouse byapplying an optimization function to a plurality of combinations ofpairs, wherein each combination of the plurality comprises a first pairincluding information specifying a first item and a first region withinthe online concierge system-specific portion of the warehouse and asecond pair including information specifying a second item and a secondregion within the online concierge system-specific portion of thewarehouse, and wherein the optimization function determines theinstructions based on a distance between the first region and the secondregion, a measure of similarity between the first item and the seconditem, and a co-occurrence of the first item and the second item inpreviously received orders; and transmitting the instructions forplacing items in regions within the online concierge system-specificregion of the warehouse from the online concierge system to a computingsystem associated with the warehouse.
 2. The method of claim 1, whereingenerating the affinity graph comprises determining the connectionbetween the pair of items based further on a weight of a co-occurrencescore calculated based on co-occurrences of items of the pair inpreviously received orders identifying the warehouse.
 3. The method ofclaim 2, wherein generating the affinity graph comprises determining theco-occurrence score by computing a sum of previously fulfilled ordersincluding one item of the pair of items and previously fulfilled ordersincluding a different item of the pair and dividing a product of aconstant and a number of previously fulfilled orders including bothitems of the pair by the sum.
 4. The method of claim 1, wherein themeasure of similarity between the first item and the second itemcomprises a measure of similarity between a first item embedding for thefirst item and a second item embedding for the second item.
 5. Themethod of claim 1, wherein generating, by the online concierge system,the affinity graph including items offered by the warehouse comprises:generating clusters of items offered by the warehouse based on distancesbetween items in the affinity graph, wherein a distance between eachpair of items in the affinity graph is based on a number ofco-occurrences of the pair of items in the previously received ordersidentifying the warehouse and a measure of similarity between the pairof items.
 6. The method of claim 5, wherein the distance between eachpair of items in the affinity graph is determined by dividing themeasure of similarity between the pair of items by a co-occurrence scoreof the pair of items, the co-occurrence score based on a number of thepreviously received orders identifying the warehouse including both ofthe pair of items.
 7. The method of claim 5, wherein the combination ofpairs includes each pair of items in a specific cluster and regionswithin the online concierge-specific portion of the warehouse.
 8. Themethod of claim 1, wherein applying the optimization function to acombination comprising the first pair including the first item and thefirst region within the online concierge system-specific portion of thewarehouse and the second pair including the second item and the secondregion within the online concierge system-specific portion of thewarehouse comprises: determining a co-occurrence value as a product of adistance between the first region and the second region, a co-occurrencescore of the first item and the second item based on a number ofco-occurrences of the first item and the second item in the previouslyreceived orders, a predicted number of orders including the first item,and a predicted number of orders including the second item; determininga similarity value by multiplying the measure of similarity between thefirst item and the second item, a predicted number of orders in whichthe first item is found, and a predicted number of orders in which thesecond item is found; and determining a value for the combination bydividing the co-occurrence value by the similarity value.
 9. The methodof claim 1, wherein generating, by the online concierge system,instructions for placing items in regions within the online conciergesystem-specific portion of the warehouse by applying the optimizationfunction to combinations of pairs comprises: ranking combinationsincluding pairs of items and regions within the online conciergesystem-specific portion of the warehouse based on corresponding valuesfrom application of the optimization function to the combinations ofpairs; selecting combinations having at least a threshold position inthe ranking; and generating the instructions specifying placement ofitems in regions within the online concierge system-specific portion ofthe warehouse according to the selected combinations.
 10. The method ofclaim 1, further comprising: receiving, at the online concierge system,a selection of an order for fulfillment at the warehouse; andtransmitting instructions for obtaining items included in the order to aclient device of the shopper, the instructions for obtaining the itemsincluded in the order identifying one or more items and correspondingregions within the online concierge system system-specific portion ofthe warehouse to the shopper.
 11. A computer program product comprisinga non-transitory computer readable storage medium having instructionsencoded thereon that, when executed by a processor, cause the processorto: generate, by an online concierge system, an affinity graph includingnodes representing items offered by a warehouse and connections betweenpairs of items offered by the warehouse, wherein a connection between apair of items is determined based on co-occurrences of items of the pairoccurring in previously received orders identifying the warehouse;generate a colocation graph for the warehouse, the colocation graphcorresponding to an online concierge system-specific portion of thewarehouse and including different regions within the online conciergesystem-specific portion of the warehouse, wherein the colocation graphincludes connections between pairs of regions within the onlineconcierge system-specific portion of the warehouse, and wherein theconnections are determined based on distances between regions within theonline concierge system-specific portion of the warehouse; generate, bythe online concierge system, instructions for placing items in regionswithin the online concierge system-specific portion of the warehouse byapplying an optimization function to a plurality of combinations ofpairs, wherein each combination of the plurality comprises a first pairincluding information specifying a first item and a first region withinthe online concierge system-specific portion of the warehouse and asecond pair including information specifying a second item and a secondregion within the online concierge system-specific portion of thewarehouse, and wherein the optimization function determines theinstructions based on a distance between the first region and the secondregion, a measure of similarity between the first item and the seconditem, and a co-occurrence of the first item and the second item inpreviously received orders; and transmit the instructions for placingitems in regions within the online concierge system-specific region ofthe warehouse from the online concierge system to a computing systemassociated with the warehouse.
 12. The computer program product of claim11, wherein generating the affinity graph comprises determining theconnection between the pair of items based further on a weight of aco-occurrence score calculated based on co-occurrences of items of thepair in previously received orders identifying the warehouse.
 13. Thecomputer program product of claim 12, wherein generating the affinitygraph comprises determining the co-occurrence score by computing a sumof previously fulfilled orders including one item of the pair of itemsand previously fulfilled orders including a different item of the pairand dividing a product of a constant and a number of previouslyfulfilled orders including both items of the pair by the sum.
 14. Thecomputer program product of claim 11, wherein the measure of similaritybetween the first item and the second item comprises a measure ofsimilarity between a first item embedding for the first item and asecond item embedding for the second item.
 15. The computer programproduct of claim 11, wherein generate, by the online concierge system,the affinity graph including items offered by the warehouse comprises:generate clusters of items offered by the warehouse based on distancesbetween items in the affinity graph, wherein a distance between eachpair of items in the affinity graph is based on a number ofco-occurrences of the pair of items in the previously received ordersidentifying the warehouse and a measure of similarity between the pairof items.
 16. The computer program product of claim 15, wherein thedistance between each pair of items in the affinity graph is determinedby dividing the measure of similarity between the pair of items by aco-occurrence score of the pair of items, the co-occurrence score basedon a number of the previously received orders identifying the warehouseincluding both of the pair of items.
 17. The method of claim 5, whereinthe combination of pairs includes each pair of items in a specificcluster and regions within the online concierge-specific portion of thewarehouse.
 18. The computer program product of claim 11, wherein applythe optimization function to a combination comprising the first pairincluding the first item and the first region within the onlineconcierge system-specific portion of the warehouse and the second pairincluding the second item and the second region within the onlineconcierge system-specific portion of the warehouse comprises: determinea co-occurrence value as a product of a distance between the firstregion and the second region, a co-occurrence score of the first itemand the second item based on a number of co-occurrences of the firstitem and the second item in the previously received orders, a predictednumber of orders including the first item, and a predicted number oforders including the second item; determine a similarity value bymultiplying the measure of similarity between the first item and thesecond item, a predicted number of orders in which the first item isfound, and a predicted number of orders in which the second item isfound; and determine a value for the combination by dividing theco-occurrence value by the similarity value.
 19. The computer programproduct of claim 11, wherein the non-transitory computer readablestorage medium further has instructions encoded thereon that, whenexecuted by the processor, cause the processor to: receive, at theonline concierge system, a selection of an order for fulfillment at thewarehouse; and transmit instructions for obtaining items included in theorder to a client device of the shopper, the instructions for obtainingthe items included in the order identifying one or more items andcorresponding regions within the online concierge system system-specificportion of the warehouse to the shopper.
 20. A system comprising: aprocessor; a non-transitory computer readable storage medium havinginstructions encoded thereon that, when executed by the processor, causethe processor to: generate, by an online concierge system, an affinitygraph including nodes representing items offered by a warehouse andconnections between pairs of items offered by the warehouse, wherein aconnection between a pair of items is determined based on co-occurrencesof items of the pair occurring in previously received orders identifyingthe warehouse; generate a colocation graph for the warehouse, thecolocation graph corresponding to an online concierge system-specificportion of the warehouse and including different regions within theonline concierge system-specific portion of the warehouse, wherein thecolocation graph includes connections between pairs of regions withinthe online concierge system-specific portion of the warehouse, andwherein the connections are determined based on distances betweenregions within the online concierge system-specific portion of thewarehouse; generate, by the online concierge system, instructions forplacing items in regions within the online concierge system-specificportion of the warehouse by applying an optimization function to aplurality of combinations of pairs, wherein each combination of theplurality comprises a first pair including information specifying afirst item and a first region within the online conciergesystem-specific portion of the warehouse and a second pair includinginformation specifying a second item and a second region within theonline concierge system-specific portion of the warehouse, and whereinthe optimization function determines the instructions based on adistance between the first region and the second region, a measure ofsimilarity between the first item and the second item, and aco-occurrence of the first item and the second item in previouslyreceived orders; and transmit the instructions for placing items inregions within the online concierge system-specific region of thewarehouse from the online concierge system to a computing systemassociated with the warehouse.